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Digital technology and new care pathways will redefine the cardiovascular workforce 数字技术和新的护理路径将重新定义心血管工作人员队伍。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-29 DOI: 10.1016/S2589-7500(24)00193-6
Virimchi Pillutla , Adam B Landman , Jagmeet P Singh
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引用次数: 0
Promises and challenges of digital tools in cardiovascular care 数字工具在心血管护理方面的前景与挑战。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-29 DOI: 10.1016/S2589-7500(24)00194-8
The Lancet Digital Health
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引用次数: 0
Digital tools in heart failure: addressing unmet needs 心力衰竭的数字化工具:满足尚未满足的需求。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-29 DOI: 10.1016/S2589-7500(24)00158-4
Prof Peder L Myhre MD PhD , Jasper Tromp MD PhD , Wouter Ouwerkerk MD PhD , Daniel S W Ting MD PhD , Kieran F Docherty MD PhD , Prof C Michael Gibson MS MD , Prof Carolyn S P Lam MD PhD
This Series paper provides an overview of digital tools in heart failure care, encompassing screening, early diagnosis, treatment initiation and optimisation, and monitoring, and the implications these tools could have for research. The current medical environment favours the implementation of digital tools in heart failure due to rapid advancements in technology and computing power, unprecedented global connectivity, and the paradigm shift towards digitisation. Despite available effective therapies for heart failure, substantial inadequacies in managing the condition have hindered improvements in patient outcomes, particularly in low-income and middle-income countries. As digital health tools continue to evolve and exert a growing influence on both clinical care and research, establishing clinical frameworks and supportive ecosystems that enable their effective use on a global scale is crucial.
本系列论文概述了心力衰竭治疗中的数字化工具,包括筛查、早期诊断、开始和优化治疗以及监测,以及这些工具可能对研究产生的影响。由于技术和计算能力的飞速发展、前所未有的全球连通性以及向数字化模式的转变,当前的医疗环境有利于在心力衰竭领域应用数字化工具。尽管心力衰竭有有效的治疗方法,但在管理心力衰竭方面存在的巨大不足阻碍了患者预后的改善,尤其是在低收入和中等收入国家。随着数字医疗工具的不断发展并对临床治疗和研究产生越来越大的影响,建立临床框架和支持性生态系统使其在全球范围内得到有效使用至关重要。
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引用次数: 0
Challenges for augmenting intelligence in cardiac imaging 在心脏成像中增强智能的挑战。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-29 DOI: 10.1016/S2589-7500(24)00142-0
Prof Partho P Sengupta MD , Prof Damini Dey PhD , Rhodri H Davies PhD , Nicolas Duchateau PhD , Naveena Yanamala PhD
Artificial Intelligence (AI), through deep learning, has brought automation and predictive capabilities to cardiac imaging. However, despite considerable investment, tangible health-care cost reductions remain unproven. Although AI holds promise, there has been insufficient time for both methodological development and prospective clinical trials to establish its advantage over human interpretations in terms of its effect on patient outcomes. Challenges such as data scarcity, privacy issues, and ethical concerns impede optimal AI training. Furthermore, the absence of a unified model for the complex structure and function of the heart and evolving domain knowledge can introduce heuristic biases and influence underlying assumptions in model development. Integrating AI into diverse institutional picture archiving and communication systems and devices also presents a clinical hurdle. This hurdle is further compounded by an absence of high-quality labelled data, difficulty sharing data between institutions, and non-uniform and inadequate gold standards for external validations and comparisons of model performance in real-world settings. Nevertheless, there is a strong push in industry and academia for AI solutions in medical imaging. This Series paper reviews key studies and identifies challenges that require a pragmatic change in the approach for using AI for cardiac imaging, whereby AI is viewed as augmented intelligence to complement, not replace, human judgement. The focus should shift from isolated measurements to integrating non-linear and complex data towards identifying disease phenotypes—emphasising pattern recognition where AI excels. Algorithms should enhance imaging reports, enriching patients' understanding, communication between patients and clinicians, and shared decision making. The emergence of professional standards and guidelines is essential to address these developments and ensure the safe and effective integration of AI in cardiac imaging.
人工智能(AI)通过深度学习为心脏成像带来了自动化和预测能力。然而,尽管投入了大量资金,但切实的医疗成本降低仍未得到证实。虽然人工智能大有可为,但还没有足够的时间来进行方法开发和前瞻性临床试验,以确定其在对患者预后的影响方面相对于人工解读的优势。数据稀缺、隐私问题和伦理问题等挑战阻碍了最佳的人工智能培训。此外,由于缺乏针对心脏复杂结构和功能的统一模型以及不断发展的领域知识,在模型开发过程中可能会出现启发式偏差并影响基本假设。将人工智能整合到不同的机构图片存档和通信系统及设备中也是一个临床障碍。缺乏高质量的标注数据、机构间难以共享数据,以及用于外部验证和比较真实世界环境中模型性能的黄金标准不统一和不充分,都进一步加剧了这一障碍。尽管如此,业界和学术界仍在大力推动医学成像领域的人工智能解决方案。这篇系列论文回顾了主要研究,并指出了在将人工智能用于心脏成像时需要务实改变方法的挑战,即把人工智能视为补充而非取代人类判断的增强智能。重点应从孤立的测量转向整合非线性和复杂的数据,以确定疾病表型--强调人工智能擅长的模式识别。算法应强化成像报告,丰富患者的理解、患者与临床医生之间的沟通以及共同决策。专业标准和指南的出现对于应对这些发展并确保人工智能安全有效地融入心脏成像至关重要。
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引用次数: 0
Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study 使用床旁高敏心肌肌钙蛋白 I 快速排除心肌梗死的机器学习算法的诊断准确性:一项回顾性研究。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-29 DOI: 10.1016/S2589-7500(24)00191-2
Betül Toprak MD , Hugo Solleder PhD , Eleonora Di Carluccio MSc , Jaimi H Greenslade PhD , Prof William A Parsonage MD , Karen Schulz DC , Prof Louise Cullen MD , Prof Fred S Apple PhD , Prof Andreas Ziegler PhD , Prof Stefan Blankenberg MD
<div><h3>Background</h3><div>Point-of-care (POC) high-sensitivity cardiac troponin (hs-cTn) assays have been shown to provide similar analytical precision despite substantially shorter turnaround times compared with laboratory-based hs-cTn assays. We applied the previously developed machine learning based personalised Artificial Intelligence in Suspected Myocardial Infarction Study (ARTEMIS) algorithm, which can predict the individual probability of myocardial infarction, with a single POC hs-cTn measurement, and compared its diagnostic performance with standard-of-care pathways for rapid rule-out of myocardial infarction.</div></div><div><h3>Methods</h3><div>We retrospectively analysed pooled data from consecutive patients of two prospective observational cohorts in geographically distinct regions (the Safe Emergency Department Discharge Rate cohort from the USA and the Suspected Acute Myocardial Infarction in Emergency cohort from Australia) who presented to the emergency department with suspected myocardial infarction. Patients with ST-segment elevation myocardial infarction were excluded. Safety and efficacy of direct rule-out of myocardial infarction by the ARTEMIS algorithm (at a pre-specified probability threshold of <0·5%) were compared with the European Society of Cardiology (ESC)-recommended and the American College of Cardiology (ACC)-recommended 0 h pathways using a single POC high-sensitivity cardiac troponin I (hs-cTnI) measurement (Siemens Atellica VTLi as investigational assay). The primary diagnostic outcome was an adjudicated index diagnosis of type 1 or type 2 myocardial infarction according to the Fourth Universal Definition of Myocardial Infarction. The safety outcome was a composite of incident myocardial infarction and cardiovascular death (follow-up events) at 30 days. Additional analyses were performed for type I myocardial infarction only (secondary diagnostic outcome), and for each cohort separately. Subgroup analyses were performed for age (<65 years <em>vs</em> ≥65 years), sex, symptom onset (≤3 h <em>vs</em> >3 h), estimated glomerular filtration rate (<60 mL/min per 1·73 m<sup>2</sup> <em>vs</em> ≥60 mL/min per 1·73 m<sup>2</sup>), and absence or presence of arterial hypertension, diabetes, a history of coronary artery disease, myocardial infarction, or heart failure, smoking, and ischaemic electrocardiogram signs.</div></div><div><h3>Findings</h3><div>Among 2560 patients (1075 [42%] women, median age 58 years [IQR 48·0–69·0]), prevalence of myocardial infarction was 6·5% (166/2560). The ARTEMIS-POC algorithm classified 899 patients (35·1%) as suitable for rapid rule-out with a negative predictive value of 99·96% (95% CI 99·64–99·96) and a sensitivity of 99·68% (97·21–99·70). For type I myocardial infarction only, negative predictive value and sensitivity were both 100%. Proportions of missed index myocardial infarction (0·05% [0·04–0·42]) and follow-up events at 30 days (0·07% [95% CI 0·06–0·59]) were l
背景:已有研究表明,与基于实验室的高敏心肌肌钙蛋白(hs-cTn)检测法相比,床旁(POC)高敏心肌肌钙蛋白(hs-cTn)检测法尽管周转时间大大缩短,但却能提供相似的分析精度。我们应用了之前开发的基于机器学习的个性化人工智能疑似心肌梗死研究(ARTEMIS)算法,该算法可通过单次 POC hs-cTn 测量预测心肌梗死的个体概率,并将其诊断性能与快速排除心肌梗死的标准护理路径进行了比较:我们回顾性分析了地理位置不同地区的两个前瞻性观察队列(美国的安全急诊科出院率队列和澳大利亚的急诊科疑似急性心肌梗死队列)中因疑似心肌梗死而到急诊科就诊的连续患者的汇总数据。ST段抬高型心肌梗死患者被排除在外。通过 ARTEMIS 算法(预先指定的概率阈值为 3 小时)、估计肾小球滤过率(2vs ≥60 mL/min 每 1-73 m2)、无或有动脉高血压、糖尿病、冠心病、心肌梗死或心力衰竭病史、吸烟和缺血性心电图体征直接排除心肌梗死的安全性和有效性:在 2560 名患者中(女性 1075 人[42%],中位年龄 58 岁[IQR 48-0-69-0]),心肌梗死发病率为 6-5%(166/2560)。ARTEMIS-POC 算法将 899 名患者(35-1%)归类为适合快速排除的患者,其阴性预测值为 99-96%(95% CI 99-64-99-96),灵敏度为 99-68%(97-21-99-70)。仅就 I 型心肌梗死而言,阴性预测值和灵敏度均为 100%。漏诊指数心肌梗死(0-05% [0-04-0-42])和30天随访事件(0-07% [95% CI 0-06-0-59])的比例较低。与指南推荐的ESC 0 h(15-2%)和ACC 0 h(13-8%)路径相比,ARTEMIS-POC算法在保持高安全性的同时,识别出的符合直接排除条件的患者人数是后者的两倍多。在所有临床相关的亚组中,疗效都很好:患者定制的医疗决策支持 ARTEMIS-POC 算法应用于单次 POC hs-cTnI 测量,与指南推荐的路径相比,可非常快速、安全、高效地直接排除心肌梗死。它有可能加快急诊科低风险患者(包括入院时症状出现不到 3 小时的早期患者)的安全出院,并有可能为疑似心肌梗死患者的分诊带来新的机遇,即使是在非住院、临床前或地理位置偏僻的医疗环境中:德国心血管研究中心(DZHK)。
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引用次数: 0
The potential for large language models to transform cardiovascular medicine 大型语言模型改变心血管医学的潜力。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-29 DOI: 10.1016/S2589-7500(24)00151-1
Giorgio Quer PhD , Prof Eric J Topol MD
Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial intelligence (AI) tools can help meet this challenge as they have considerable potential for early diagnosis and prediction of occurrence of these diseases. Deep neural networks can improve the accuracy of medical image interpretation and their outputs can provide rich information that otherwise would not be detected by cardiologists. With recent advances in transformer models, multimodal AI, and large language models, the ability to integrate electronic health record data with images, genomics, biosensors, and other data has the potential to improve diagnosis and partition patients who are at high risk for primary preventive strategies. Although much emphasis has been placed on AI supporting clinicians, AI can also serve patients and provide immediate help with diagnosis, such as that of arrhythmia, and is being studied for automated self-imaging. Potential risks, such as loss of data privacy or potential diagnostic errors, should be addressed before use in clinical practice. This Series paper explores opportunities and limitations of AI models for cardiovascular medicine, and aims to identify specific barriers to and solutions in the application of AI models, facilitating their integration into health-care systems.
心血管疾病一直是全球死亡的主要原因,其早期检测和预测仍然是一项重大挑战。人工智能(AI)工具可以帮助应对这一挑战,因为它们在早期诊断和预测这些疾病的发生方面具有相当大的潜力。深度神经网络可以提高医学图像解读的准确性,其输出结果可以提供丰富的信息,否则心脏病专家将无法检测到这些信息。随着变压器模型、多模态人工智能和大型语言模型的最新进展,将电子健康记录数据与图像、基因组学、生物传感器和其他数据进行整合的能力有望改善诊断,并将高风险患者分区,以采取初级预防策略。虽然人工智能的重点是为临床医生提供支持,但人工智能也可以为患者服务,为诊断(如心律失常)提供即时帮助,目前正在研究自动自我成像。在用于临床实践之前,应解决潜在的风险,如数据隐私的丢失或潜在的诊断错误。本系列论文探讨了心血管医学人工智能模型的机遇和局限性,旨在找出应用人工智能模型的具体障碍和解决方案,促进人工智能模型与医疗保健系统的整合。
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引用次数: 0
Mitigating the risk of artificial intelligence bias in cardiovascular care 降低心血管护理中人工智能偏差的风险。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-29 DOI: 10.1016/S2589-7500(24)00155-9
Ariana Mihan MPH , Ambarish Pandey MD , Harriette GC Van Spall MD
Digital health technologies can generate data that can be used to train artificial intelligence (AI) algorithms, which have been particularly transformative in cardiovascular health-care delivery. However, digital and health-care data repositories that are used to train AI algorithms can introduce bias when data are homogeneous and health-care processes are inequitable. AI bias can also be introduced during algorithm development, testing, implementation, and post-implementation processes. The consequences of AI algorithmic bias can be considerable, including missed diagnoses, misclassification of disease, incorrect risk prediction, and inappropriate treatment recommendations. This bias can disproportionately affect marginalised demographic groups. In this Series paper, we provide a brief overview of AI applications in cardiovascular health care, discuss stages of algorithm development and associated sources of bias, and provide examples of harm from biased algorithms. We propose strategies that can be applied during the training, testing, and implementation of AI algorithms to mitigate bias so that all those at risk for or living with cardiovascular disease might benefit equally from AI.
数字医疗技术可以生成用于训练人工智能(AI)算法的数据,这些数据在心血管医疗服务领域尤其具有变革性。然而,用于训练人工智能算法的数字和医疗保健数据存储库可能会在数据同质化和医疗保健流程不公平的情况下引入偏差。在算法开发、测试、实施和实施后的过程中,也可能引入人工智能偏见。人工智能算法偏差的后果可能相当严重,包括漏诊、疾病分类错误、风险预测错误和治疗建议不当。这种偏差会对边缘化人口群体造成极大影响。在这篇系列论文中,我们简要概述了人工智能在心血管医疗保健中的应用,讨论了算法开发的各个阶段和相关的偏见来源,并提供了有偏见的算法造成伤害的实例。我们提出了可在人工智能算法的训练、测试和实施过程中应用的策略,以减少偏差,从而使所有心血管疾病高危人群或患者都能平等地受益于人工智能。
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引用次数: 0
Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis 评估风险预测模型以选择欧洲肺癌筛查参与者:前瞻性队列联合分析
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-21 DOI: 10.1016/S2589-7500(24)00123-7
Xiaoshuang Feng PhD , Patrick Goodley MBBCh , Karine Alcala MS , Florence Guida PhD , Prof Rudolf Kaaks PhD , Prof Roel Vermeulen PhD , George S Downward PhD , Catalina Bonet MSc , Sandra M Colorado-Yohar PhD , Demetrius Albanes MD , Stephanie J Weinstein PhD , Prof Marcel Goldberg PhD , Prof Marie Zins PhD , Prof Caroline Relton PhD , Prof Arnulf Langhammer PhD , Anne Heidi Skogholt PhD , Mattias Johansson PhD , Hilary A Robbins PhD
<div><h3>Background</h3><p>Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts.</p></div><div><h3>Methods</h3><p>We analysed 240 137 participants aged 45–80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCO<sub>m2012</sub>), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London—Death (UCLD), the University College London—Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) criteria.</p></div><div><h3>Findings</h3><p>Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCO<sub>m2012</sub>, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59–0·77) to 0·83 (0·78–0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57–0·72) to 0·78 (0·74–0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a
背景肺癌风险预测模型可有效识别应接受肺癌筛查的人群。然而,欧洲尚未对这些模型的性能进行全面评估。我们的目的是在欧洲前瞻性队列中对几种预测肺癌发病率或死亡率的风险预测模型的性能进行外部验证和评估。方法我们分析了肺癌队列联合会(Lung Cancer Cohort Consortium)集合数据库中四个前瞻性队列中来自九个欧洲国家的 240 137 名 45-80 岁、目前或曾经有吸烟史的参与者,这四个前瞻性队列分别是:α-生育酚、β-胡萝卜素癌症预防研究(芬兰)、北特伦德拉格健康研究(挪威)、CONSTANCES(法国)和欧洲癌症与营养前瞻性调查(丹麦、德国、意大利、西班牙、瑞典、荷兰和挪威)。我们评估了十种肺癌风险模型,包括巴赫模型、前列腺癌、肺癌、结肠直肠癌和卵巢癌筛查试验 2012 模型 (PLCOm2012)、肺癌风险评估工具 (LCRAT)、肺癌死亡风险评估工具 (LCDRAT)、北特伦德拉格健康研究 (HUNT)、肺癌风险评估工具 (LCRAT)、肺癌死亡风险评估工具 (LCDRAT)、肺癌早期预警优化模型 (HUNT)、肺癌风险评估工具 (LCRAT)、肺癌死亡风险评估工具 (LCDRAT)、肺癌风险优化预警模型 (OWL)、伦敦大学学院死亡模型 (UCLD)、伦敦大学学院发病模型 (UCLI)、利物浦肺项目第二版模型 (LLP 第二版) 和利物浦肺项目第三版模型 (LLP 第三版)。我们用预期病例或死亡病例与观察病例或死亡病例之比来量化模型校准,并用接收者工作特征曲线下面积(AUC)来区分模型。对于每个模型,我们还确定了与美国预防服务工作组 2021 年(USPSTF-2021)、美国预防服务工作组 2013 年(USPSTF-2013)和 Nederlands-Leuvens Longkanker Screenings Onderzoek(NELSON)标准相同的筛查人数的风险阈值。在大多数国家,大多数模型都具有合理的校准,但在 8 个国家,LLP 第 2 版对风险的预测超过了 50%(预期与观察值之比≥1-50)。PLCOm2012、LCDRAT、LCRAT、Bach、HUNT、OWL、UCLD 和 UCLI 模型在大多数国家显示出相似的区分度,AUC 从 0-68(95% CI 0-59-0-77)到 0-83(0-78-0-89)不等,而 LLP 版本 2 和 LLP 版本 3 显示出较低的区分度,AUC 从 0-64(95% CI 0-57-0-72)到 0-78(0-74-0-83)不等。在汇总所有国家的数据(但不包括 HUNT 队列)时,33-9% 的患者(216 387 例中的 73 313 例)符合 USPSTF-2021 标准,其中包括 74-8% 的肺癌患者(1185 例)和 76-3% 的 5 年以上肺癌死亡患者(730 例)。根据 USPSTF-2013 和 NELSON 标准,符合条件的人数较少。在应用阈值选择与 USPSTF-2021 相同规模的人群后,PLCOm2012、LCDRAT、LCRAT、Bach、HUNT、OWL、UCLD 和 UCLI 模型识别了 77%-6%-79-1% 的未来病例,尽管与 USPSTF-2021 标准相比,它们选择的个体年龄稍大。USPSTF-2013 和 NELSON 的结果相似。解释:在欧洲国家,几种肺癌风险预测模型表现良好,如果用来代替分类资格标准,可能会提高肺癌筛查的效率。
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引用次数: 0
A future role for health applications of large language models depends on regulators enforcing safety standards 大型语言模型未来在健康领域的应用取决于监管机构是否执行安全标准
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-21 DOI: 10.1016/S2589-7500(24)00124-9
Oscar Freyer , Isabella Catharina Wiest Dr med , Prof Jakob Nikolas Kather Dr med , Stephen Gilbert PhD

Among the rapid integration of artificial intelligence in clinical settings, large language models (LLMs), such as Generative Pre-trained Transformer-4, have emerged as multifaceted tools that have potential for health-care delivery, diagnosis, and patient care. However, deployment of LLMs raises substantial regulatory and safety concerns. Due to their high output variability, poor inherent explainability, and the risk of so-called AI hallucinations, LLM-based health-care applications that serve a medical purpose face regulatory challenges for approval as medical devices under US and EU laws, including the recently passed EU Artificial Intelligence Act. Despite unaddressed risks for patients, including misdiagnosis and unverified medical advice, such applications are available on the market. The regulatory ambiguity surrounding these tools creates an urgent need for frameworks that accommodate their unique capabilities and limitations. Alongside the development of these frameworks, existing regulations should be enforced. If regulators fear enforcing the regulations in a market dominated by supply or development by large technology companies, the consequences of layperson harm will force belated action, damaging the potentiality of LLM-based applications for layperson medical advice.

在人工智能与临床环境的快速融合中,大型语言模型(LLMs),如生成预训练转换器-4,已成为一种多方面的工具,在医疗保健服务、诊断和病人护理方面具有潜力。然而,LLMs 的部署引发了大量的监管和安全问题。由于其输出可变性高、内在可解释性差以及所谓的人工智能幻觉风险,根据美国和欧盟法律(包括最近通过的《欧盟人工智能法案》),基于 LLM 的医疗保健应用在作为医疗设备获得批准时面临监管挑战。尽管患者面临的风险(包括误诊和未经验证的医疗建议)尚未得到解决,但市场上仍有此类应用程序。围绕这些工具的监管模糊性导致迫切需要制定框架,以适应其独特的能力和局限性。在制定这些框架的同时,应执行现有法规。如果监管者害怕在由大型技术公司主导供应或开发的市场中执行法规,那么外行人受到伤害的后果将迫使监管者迟迟不采取行动,从而损害基于 LLM 的外行人医疗建议应用的潜力。
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引用次数: 0
Balancing AI innovation with patient safety 平衡人工智能创新与患者安全
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-21 DOI: 10.1016/S2589-7500(24)00175-4
The Lancet Digital Health
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Lancet Digital Health
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